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Showing results 1 to 20 of 37
   Efficient Hessian-based DNN Optimization via Chain-Rule ApproximationTemperoni, Alessandro  ; Dalle Lucca Tosi, Mauro ; Theobald, Martin in Proceedings of the 6th Joint International Conference on Data Science Management of Data (10th ACM IKDD CODS and 28th COMAD) (2023) Detailed reference viewed: 36 (8 UL) Targeting a light-weight and multi-channel approach for distributed stream processing; Theobald, Martin ; et alin Journal of Parallel and Distributed Computing (2022), 167 Detailed reference viewed: 30 (1 UL)Robust and Provable Guarantees for Sparse Random Embeddings; Temperoni, Alessandro ; Theobald, Martin in Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II. (2022, May 18) Detailed reference viewed: 25 (1 UL)CONVERGENCE TIME ANALYSIS OF ASYNCHRONOUS DISTRIBUTED ARTIFICIAL NEURAL NETWORKSDalle Lucca Tosi, Mauro ; Ellampallil Venugopal, Vinu ; Theobald, Martin Poster (2022) Detailed reference viewed: 31 (6 UL)Convergence time analysis of Asynchronous Distributed Artificial Neural NetworksDalle Lucca Tosi, Mauro ; ; Theobald, Martin in 5th Joint International Conference on Data Science Management of Data (9th ACM IKDD CODS and 27th COMAD) (2022) Detailed reference viewed: 48 (10 UL)Revisiting Weight Initialization of Deep Neural Networks; Temperoni, Alessandro ; Theobald, Martin in Proceedings of Machine Learning Research (2021, November 17) Detailed reference viewed: 20 (0 UL)Online Learning using Distributed Neural NetworksDalle Lucca Tosi, Mauro ; Theobald, Martin ; Ellampallil Venugopal, Vinu Poster (2021, May 21) Detailed reference viewed: 96 (7 UL)Asynchronous Stream Data Processing using a Light-Weight and High-Performance Dataflow EngineEllampallil Venugopal, Vinu ; Theobald, Martin Presentation (2020, December 11) Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous ... [more ▼] Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous progress in distributed stream processing systems in the past few years, the high-throughput and low-latency (a.k.a. high sustainable-throughput) requirement of modern applications is pushing the limits of traditional data processing infrastructures. This paper introduces a new distributed stream data processing engine (DSPE), called “Asynchronous Iterative Routing” or simply AIR, which implements a light-weight, dynamic sharding protocol. AIR expedites a direct and asynchronous communication among all the worker nodes via multiple Message Passing Interface (MPI) communication channels and thereby completely avoids any additional communication overhead with a dedicated master node. With its unique design, AIR scales out to clusters consisting of up to 8 nodes and 224 cores, performing much better than existing DSPEs, and it performs up to 15 times better than Spark and Flink in terms of sustainable-throughput. [less ▲] Detailed reference viewed: 105 (6 UL)Effective Stream Data Processing using Asynchronous Iterative Routing ProtocolEllampallil Venugopal, Vinu ; Theobald, Martin Poster (2020, December 10) Detailed reference viewed: 71 (6 UL)27th International Symposium on Temporal Representation and Reasoning, TIME 2020, September 23-25, 2020, Bozen-Bolzano, Italy; ; Theobald, Martin Book published by Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020) Detailed reference viewed: 81 (18 UL)AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative RoutingEllampallil Venugopal, Vinu ; Theobald, Martin ; Chaychi, Samira et alin AIR: A Light-Weight Yet High-Performance Dataflow Engine based on Asynchronous Iterative Routing (2020, September 01) Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow ... [more ▼] Distributed Stream Processing Engines (DSPEs) are currently among the most emerging topics in data management, with applications ranging from real-time event monitoring to processing complex dataflow programs and big data analytics. In this paper, we describe the architecture of our AIR engine, which is designed from scratch in C++ using the Message Passing Interface (MPI), pthreads for multithreading, and is directly deployed on top of a common HPC workload manager such as SLURM. AIR implements a light-weight, dynamic sharding protocol (referred to as “Asynchronous Iterative Routing”), which facilitates a direct and asynchronous communication among all worker nodes and thereby completely avoids any additional communication overhead with a dedicated master node. With its unique design, AIR fills the gap between the prevalent scale-out (but Java-based) architectures like Apache Spark and Flink, on one hand, and recent scale-up (and C++ based) prototypes such as StreamBox and PiCo, on the other hand. Our experiments over various benchmark settings confirm that AIR performs as good as the best scale-up SPEs on a single-node setup, while it outperforms existing scale-out DSPEs in terms of processing latency and sustainable throughput by a factor of up to 15 in a distributed setting. [less ▲] Detailed reference viewed: 99 (13 UL)Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback; ; et al in Guided Inductive Logic Programming: Cleaning Knowledge Bases with Iterative User Feedback (2020, March 12) Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both ... [more ▼] Domain-oriented knowledge bases (KBs) such as DBpedia and YAGO are largely constructed by applying a set of predefined extraction rules to the semi-structured contents of Wikipedia articles. Although both of these large-scale KBs achieve very high average precision values (above 95% for YAGO3), subtle mistakes in a few of the underlying extraction rules may still impose a substantial amount of systematic extraction mistakes for specific relations. For example, by applying the same regular expressions to extract person names of both Asian and Western nationality, YAGO erroneously swaps most of the family and given names of Asian person entities. For traditional rule-learning approaches based on Inductive Logic Programming (ILP), it is very difficult to detect these systematic extraction mistakes, since they usually occur only in a relatively small subdomain of the relations’ arguments. In this paper, we thus propose a guided form of ILP, coined “GILP”, that iteratively asks for small amounts of user feedback over a given KB to learn a set of data-cleaning rules that (1) best match the feedback and (2) also generalize to a larger portion of facts in the KB. We propose both algorithms and respective metrics to automatically assess the quality of the learned rules with respect to the user feedback. [less ▲] Detailed reference viewed: 65 (3 UL)Benchmarking Synchronous and Asynchronous Stream Processing SystemsEllampallil Venugopal, Vinu ; Theobald, Martin in Ellampallil Venugopal, Vinu; Theobald, Martin (Eds.) Benchmarking Synchronous and Asynchronous Stream Processing Systems (2020, January 02) Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous ... [more ▼] Processing high-throughput data-streams has become a major challenge in areas such as real-time event monitoring, complex dataflow processing, and big data analytics. While there has been tremendous progress in distributed stream processing systems in the past few years, the high-throughput and low-latency (a.k.a. high sustainable-throughput) requirement of modern applications is pushing the limits of traditional data processing infrastructures. To understand the upper bound of the maximum sustainable throughput that is possible for a given node configuration, we have designed multiple hard-coded multi-threaded processes (called ad-hoc dataflows) in C++ using Message Passing Interface (MPI) and Pthread libraries. Our preliminary results show that our ad-hoc design on average is 5.2 times better than Flink and 9.3 times better than Spark. [less ▲] Detailed reference viewed: 98 (7 UL)Scalable Uncertainty Management - 13th International Conference (SUM 2019), Compiegne, France, December 16-18, 2019, Proceedings; ; Theobald, Martin Book published by Springer (2019) Detailed reference viewed: 58 (4 UL)Outer and Anti Joins in Temporal-Probabilistic Databases; Theobald, Martin ; in 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019 (2019, October 16) Detailed reference viewed: 83 (2 UL)Lineage-Aware Temporal Windows: Supporting Set Operations in Temporal-Probabilistic Databases; Theobald, Martin ; in CoRR (2019), abs/1910.00474 Detailed reference viewed: 35 (2 UL)Anytime Approximation in Probabilistic Databases via Scaled Dissociations; ; et al in Proceedings of the 2019 International Conference on Management of Data, SIGMOD Conference 2019, Amsterdam, The Netherlands, June 30 - July 5, 2019 (2019, June 22) Detailed reference viewed: 58 (1 UL)Generalized Lineage-Aware Temporal Windows: Supporting Outer and Anti Joins in Temporal-Probabilistic Databases; Theobald, Martin ; in CoRR (2019), abs/1902.04379 Detailed reference viewed: 37 (1 UL)From Big Data to Big Knowledge - Large-Scale Information Extraction Based on Statistical Methods (Invited Talk)Theobald, Martin Scientific Conference (2019, May 14) Detailed reference viewed: 62 (4 UL)Indexing for Graph Query Evaluation; Theobald, Martin in Sakr, Sharif; Zomaya, Albert Y. (Eds.) Encyclopedia of Big Data Technologies (2019) Detailed reference viewed: 93 (3 UL)
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